Showing papers by "L. Jeff Hong published in 2021"

Review on ranking and selection: A new perspective

Abstract: In this paper, we briefly review the development of ranking and selection (R&S) in the past 70 years, especially the theoretical achievements and practical applications in the past 20 years. Different from the frequentist and Bayesian classifications adopted by Kim and Nelson (2006b) and Chick (2006) in their review articles, we categorize existing R&S procedures into fixed-precision and fixed-budget procedures, as in Hunter and Nelson (2017). We show that these two categories of procedures essentially differ in the underlying methodological formulations, i.e., they are built on hypothesis testing and dynamic programming, respectively. In light of this variation, we review in detail some well-known procedures in the literature and show how they fit into these two formulations. In addition, we discuss the use of R&S procedures in solving various practical problems and propose what we think are the important research questions in the field.

Knockout-Tournament Procedures for Large-Scale Ranking and Selection in Parallel Computing Environments

Abstract: On one hand, large-scale ranking and selection (R&S) problems require a large amount of computation. On the other hand, parallel computing environments that provide a large capacity for computation...

Ranking and Selection with Covariates for Personalized Decision Making

Abstract: We consider a problem of ranking and selection via simulation in the context of personalized decision making, in which the best alternative is not universal, but varies as a function of some observ...

Speeding Up Paulson’s Procedure for Large-Scale Problems Using Parallel Computing

Abstract: With the rapid development of computing technology, using parallel computing to solve large-scale ranking-and-selection (R&S) problems has emerged as an important research topic. However, direct im...

Learning-Based Robust Optimization: Procedures and Statistical Guarantees

Abstract: Robust optimization (RO) is a common approach to tractably obtain safeguarding solutions for optimization problems with uncertain constraints. In this paper, we study a statistical framework to int...

Technical note—Knowledge gradient for selection with covariates: Consistency and computation

Abstract: Knowledge gradient is a design principle for developing Bayesian sequential sampling policies to solve optimization problems. In this paper we consider the ranking and selection problem in the presence of covariates, where the best alternative is not universal but depends on the covariates. In this context, we prove that under minimal assumptions, the sampling policy based on knowledge gradient is consistent, in the sense that following the policy the best alternative as a function of the covariates will be identified almost surely as the number of samples grows. We also propose a stochastic gradient ascent algorithm for computing the sampling policy and demonstrate its performance via numerical experiments.

Meaningful sensitivities: A new family of simulation sensitivity measures

Abstract: Sensitivity analysis quantifies how a model output responds to variations in its inputs. However, the following sensitivity question has never been rigorously answered: How sensitive is the mean or...

A New Likelihood Ratio Method for Training Artificial Neural Networks

Abstract: We investigate a new approach to compute the gradients of artificial neural networks (ANNs), based on the so-called push-out likelihood ratio method. Unlike the widely used backpropagation (BP) met...

Earning and Learning with Varying Cost

Abstract: We study a dynamic pricing problem where the observed cost in each selling period varies from period to period, and the demand function is unknown and only depends on the price. The decision maker needs to select a price from a menu of K prices in each period to maximize the expected cumulative profit. Motivated by the classical upper confidence bound (UCB) algorithm for the multi‐armed bandit problem, we propose a UCB‐Like policy to select the price. When the cost is a continuous random variable, as the cost varies, the profit of the optimal price can be arbitrarily close to that of the second‐best price, making it very difficult to make the correct decision. In this situation, we show that the expected cumulative regret of our policy grows in the order of (log T)2, where T is the number of selling periods. When the cost takes discrete values from a finite set and all prices are optimal for some costs, we show that the expected cumulative regret is upper bounded by a constant for any T. This result suggests that in this situation, the suboptimal price will only be selected in a finite number of periods, and the trade‐off between earning and learning vanishes and learning is no longer necessary beyond a certain period.

Surrogate-Based Simulation Optimization

Abstract: Simulation models are widely used in practice to facilitate decision-making in a complex, dynamic and stochastic environment. But they are computationally expensive to execute and optimize, due to lack of analytical tractability. Simulation optimization is concerned with developing efficient sampling schemes -- subject to a computational budget -- to solve such optimization problems. To mitigate the computational burden, surrogates are often constructed using simulation outputs to approximate the response surface of the simulation model. In this tutorial, we provide an up-to-date overview of surrogate-based methods for simulation optimization with continuous decision variables. Typical surrogates, including linear basis function models and Gaussian processes, are introduced. Surrogates can be used either as a local approximation or a global approximation. Depending on the choice, one may develop algorithms that converge to either a local optimum or a global optimum. Representative examples are presented for each category. Recent advances in large-scale computation for Gaussian processes are also discussed.